Neural network basics
        Introduction to Artificial Intelligence and Machine Learning
        Basic concepts and history of neural networks

    Fully Connected Network Architecture
        The definition and function of the fully connected layer
        Feedforward and feedback mechanisms of the network

    The role of activation function
        Comparison of different activation functions (ReLU, sigmoid, tanh, etc.)
        The importance of activation functions in networks

    Training a fully connected network
        Loss function selection and optimization algorithm
        Overfitting problem and its solution

    Case studies and applications
        Applications of fully connected networks in image recognition, natural language processing and other fields
        Actual case analysis

    Practical operation
        Build a simple fully connected network
        Demo using Python and popular machine learning libraries

    Future Outlook and Discussion
        Limitations and future development directions of fully connected networks
        Q&A session, interact with the speakers